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feat: backend model load balancing support (#4927)

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takatost 2024-06-05 00:13:04 +08:00 • committed by GitHub
commit d1dbbc1e33
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47 changed files with 2191 additions and 256 deletions
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9
api/core/rag/docstore/dataset_docstore.py
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@ -1,11 +1,10 @@
from collections.abc import Sequence
from typing import Any, Optional, cast
from typing import Any, Optional
from sqlalchemy import func
from core.model_manager import ModelManager
from core.model_runtime.entities.model_entities import ModelType
from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
from core.rag.models.document import Document
from extensions.ext_database import db
from models.dataset import Dataset, DocumentSegment
@ -95,11 +94,7 @@ class DatasetDocumentStore:
# calc embedding use tokens
if embedding_model:
model_type_instance = embedding_model.model_type_instance
model_type_instance = cast(TextEmbeddingModel, model_type_instance)
tokens = model_type_instance.get_num_tokens(
model=embedding_model.model,
credentials=embedding_model.credentials,
tokens = embedding_model.get_text_embedding_num_tokens(
texts=[doc.page_content]
)
else:

9
api/core/rag/splitter/fixed_text_splitter.py
View file

@ -1,10 +1,9 @@
"""Functionality for splitting text."""
from __future__ import annotations
from typing import Any, Optional, cast
from typing import Any, Optional
from core.model_manager import ModelInstance
from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
from core.model_runtime.model_providers.__base.tokenizers.gpt2_tokenzier import GPT2Tokenizer
from core.rag.splitter.text_splitter import (
TS,
@ -35,11 +34,7 @@ class EnhanceRecursiveCharacterTextSplitter(RecursiveCharacterTextSplitter):
return 0
if embedding_model_instance:
embedding_model_type_instance = embedding_model_instance.model_type_instance
embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance)
return embedding_model_type_instance.get_num_tokens(
model=embedding_model_instance.model,
credentials=embedding_model_instance.credentials,
return embedding_model_instance.get_text_embedding_num_tokens(
texts=[text]
)
else: